Quick Start Guide

This guide walks you through making your first FinBrain API calls. By the end, you’ll know how to retrieve AI price predictions, insider trading data, and sentiment scores.

Prerequisites

A FinBrain API key (sign up here)
Python 3.7+ (for SDK) or any HTTP client

Step 1: Install the Python SDK

The fastest way to get started is with our official Python SDK:

pip install finbrain-python

Step 2: Initialize the Client

from finbrain import FinBrainClient

# Replace with your actual API key
fb = FinBrainClient(api_key="YOUR_API_KEY")

Step 3: Fetch Data

Get AI Price Predictions

Retrieve deep learning price forecasts for any ticker:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

# Get daily predictions for Apple as DataFrame (recommended)
df = fb.predictions.ticker("AAPL", prediction_type="daily",
                           as_dataframe=True)
print(df)
#                   main    lower    upper
# date
# 2024-11-04     201.33   197.21   205.45
# 2024-11-05     202.77   196.92   208.61
# 2024-11-06     203.99   196.90   211.08

# Or get raw JSON response
predictions = fb.predictions.ticker("AAPL", prediction_type="daily")
print(predictions)

Sample JSON Output:

{
  "ticker": "AAPL",
  "name": "Apple Inc.",
  "prediction": {
    "2024-11-04": "201.33,197.21,205.45",
    "2024-11-05": "202.77,196.92,208.61",
    "2024-11-06": "203.99,196.90,211.08",
    "expectedShort": "0.22",
    "expectedMid": "0.58",
    "expectedLong": "0.25",
    "type": "daily",
    "lastUpdate": "2024-11-01T23:24:18.371Z"
  },
  "sentimentAnalysis": {
    "2024-11-04": "0.186",
    "2024-11-01": "0.339",
    "2024-10-31": "0.565"
  }
}

const response = await fetch(
  "https://api.finbrain.tech/v1/ticker/AAPL/predictions/daily?token=YOUR_API_KEY"
);
const data = await response.json();
console.log(data);

curl "https://api.finbrain.tech/v1/ticker/AAPL/predictions/daily?token=YOUR_API_KEY"

Get Insider Trading Data

Track executive purchases and sales from SEC Form 4 filings:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

# Get insider transactions for Tesla as DataFrame (recommended)
df = fb.insider_transactions.ticker("S&P 500", "TSLA",
                                    as_dataframe=True)
print(df.head())
#                 insiderTradings  relationship  transaction  shares    cost     USDValue
# date
# 2024-01-10          Elon Musk           CEO         Sale   50000  245.50     12275000

# Or get raw JSON response
insiders = fb.insider_transactions.ticker("S&P 500", "TSLA")
print(insiders)

Sample JSON Output:

{
  "ticker": "TSLA",
  "name": "Tesla Inc.",
  "insiderTransactions": [
    {
      "date": "2024-01-10",
      "insiderTradings": "Elon Musk",
      "relationship": "CEO",
      "transaction": "Sale",
      "cost": 245.50,
      "shares": 50000,
      "USDValue": 12275000,
      "totalShares": 715000000,
      "SECForm4Date": "2024-01-11",
      "SECForm4Link": "https://sec.gov/..."
    }
  ]
}

const response = await fetch(
  "https://api.finbrain.tech/v1/insidertransactions/S%26P%20500/TSLA?token=YOUR_API_KEY"
);
const data = await response.json();
console.log(data);

curl "https://api.finbrain.tech/v1/insidertransactions/S%26P%20500/TSLA?token=YOUR_API_KEY"

Get Sentiment Scores

Access AI-powered sentiment analysis from financial news:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

# Get sentiment for Microsoft as DataFrame (recommended)
df = fb.sentiments.ticker("S&P 500", "MSFT", as_dataframe=True)
print(df.tail())
#             sentiment
# date
# 2024-01-15      0.72
# 2024-01-14      0.68

# Or get raw JSON response
sentiment = fb.sentiments.ticker("S&P 500", "MSFT")
print(sentiment)

Sample JSON Output:

{
  "ticker": "MSFT",
  "name": "Microsoft Corporation",
  "sentimentAnalysis": {
    "2024-01-15": "0.72",
    "2024-01-14": "0.68",
    "2024-01-13": "0.65",
    "2024-01-12": "0.70",
    "2024-01-11": "0.63"
  }
}

const response = await fetch(
  "https://api.finbrain.tech/v1/sentiments/S%26P%20500/MSFT?token=YOUR_API_KEY"
);
const data = await response.json();
console.log(data);

curl "https://api.finbrain.tech/v1/sentiments/S%26P%20500/MSFT?token=YOUR_API_KEY"

Get Congressional Trades

Monitor US House Representatives trading activity:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

# Get congressional trades for NVIDIA as DataFrame (recommended)
df = fb.house_trades.ticker("S&P 500", "NVDA", as_dataframe=True)
print(df.head())
#                representative       type                   amount
# date
# 2024-01-08      Nancy Pelosi  Purchase  $1,000,001 - $5,000,000

# Or get raw JSON response
trades = fb.house_trades.ticker("S&P 500", "NVDA")
print(trades)

Sample JSON Output:

{
  "ticker": "NVDA",
  "name": "NVIDIA Corporation",
  "houseTrades": [
    {
      "date": "2024-01-08",
      "representative": "Nancy Pelosi",
      "type": "Purchase",
      "amount": "$1,000,001 - $5,000,000"
    }
  ]
}

const response = await fetch(
  "https://api.finbrain.tech/v1/housetrades/S%26P%20500/NVDA?token=YOUR_API_KEY"
);
const data = await response.json();
console.log(data);

curl "https://api.finbrain.tech/v1/housetrades/S%26P%20500/NVDA?token=YOUR_API_KEY"

Step 4: Filter by Date Range

Most endpoints support date filtering:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

# Get sentiment for a specific date range as DataFrame
df = fb.sentiments.ticker(
    "S&P 500",
    "AAPL",
    date_from="2024-01-01",
    date_to="2024-01-31",
    as_dataframe=True
)
print(df)

Or with the REST API:

curl "https://api.finbrain.tech/v1/ticker/AAPL/predictions/daily?token=YOUR_API_KEY&dateFrom=2024-01-01&dateTo=2024-01-31"

Complete Example

Here’s a complete script that fetches multiple datasets as DataFrames:

from finbrain import FinBrainClient

# Initialize client
fb = FinBrainClient(api_key="YOUR_API_KEY")

# Define ticker to analyze
ticker = "AAPL"
market = "S&P 500"

print(f"Analyzing {ticker}...\n")

# AI Predictions
predictions_df = fb.predictions.ticker(ticker,
                                       prediction_type="daily",
                                       as_dataframe=True)
print("AI Predictions:")
print(predictions_df.head())

# Insider Transactions
insiders_df = fb.insider_transactions.ticker(market, ticker,
                                             as_dataframe=True)
print("\nInsider Transactions:")
print(insiders_df.head())

# Sentiment
sentiment_df = fb.sentiments.ticker(market, ticker,
                                    as_dataframe=True)
print("\nSentiment Scores:")
print(sentiment_df.tail())

# Analyst Ratings
ratings_df = fb.analyst_ratings.ticker(market, ticker,
                                       as_dataframe=True)
print("\nAnalyst Ratings:")
print(ratings_df.head())

Next Steps

Now that you’ve made your first API calls, explore more:

Datasets - Detailed dataset documentation
API Reference - Complete endpoint reference
Python SDK - Full SDK documentation
Guides - Build trading strategies